Disrupted Iron Homeostasis Causes Dopaminergic Neurodegeneration in Mice
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Expression Gene Network Analyses Reveal Molecular Mechanisms And
www.nature.com/scientificreports OPEN Diferential expression and co- expression gene network analyses reveal molecular mechanisms and candidate biomarkers involved in breast muscle myopathies in chicken Eva Pampouille1,2, Christelle Hennequet-Antier1, Christophe Praud1, Amélie Juanchich1, Aurélien Brionne1, Estelle Godet1, Thierry Bordeau1, Fréderic Fagnoul2, Elisabeth Le Bihan-Duval1 & Cécile Berri1* The broiler industry is facing an increasing prevalence of breast myopathies, such as white striping (WS) and wooden breast (WB), and the precise aetiology of these occurrences remains poorly understood. To progress our understanding of the structural changes and molecular pathways involved in these myopathies, a transcriptomic analysis was performed using an 8 × 60 K Agilent chicken microarray and histological study. The study used pectoralis major muscles from three groups: slow-growing animals (n = 8), fast-growing animals visually free from defects (n = 8), or severely afected by both WS and WB (n = 8). In addition, a weighted correlation network analysis was performed to investigate the relationship between modules of co-expressed genes and histological traits. Functional analysis suggested that selection for fast growing and breast meat yield has progressively led to conditions favouring metabolic shifts towards alternative catabolic pathways to produce energy, leading to an adaptive response to oxidative stress and the frst signs of infammatory, regeneration and fbrosis processes. All these processes are intensifed in muscles afected by severe myopathies, in which new mechanisms related to cellular defences and remodelling seem also activated. Furthermore, our study opens new perspectives for myopathy diagnosis by highlighting fne histological phenotypes and genes whose expression was strongly correlated with defects. Te poultry industry relies on the production of fast-growing chickens, which are slaughtered at high weights and intended for cutting and processing. -
Product Data Sheet
Product Data Sheet ExProfileTM Human AMPK Signaling Related Gene qPCR Array For focused group profiling of human AMPK signaling genes expression Cat. No. QG004-A (4 x 96-well plate, Format A) Cat. No. QG004-B (4 x 96-well plate, Format B) Cat. No. QG004-C (4 x 96-well plate, Format C) Cat. No. QG004-D (4 x 96-well plate, Format D) Cat. No. QG004-E (4 x 96-well plate, Format E) Plates available individually or as a set of 6. Each set contains 336 unique gene primer pairs deposited in one 96-well plate. Introduction The ExProfile human AMPK signaling related gene qPCR array profiles the expression of 336 human genes related to AMPK-mediated signal transduction. These genes are carefully chosen for their close pathway correlation based on a thorough literature search of peer-reviewed publications, mainly including genes that encode AMP-activated protein kinase complex,its regulators and targets involved in many important biological processes, such as glucose uptake, β-oxidation of fatty acids and modulation of insulin secretion. This array allows researchers to study the pathway-related genes to gain understanding of their roles in the different biological processes. QG004 plate 01: 84 unique gene PCR primer pairs QG004 plate 02: 84 unique gene PCR primer pairs QG004 plate 03: 84 unique gene PCR primer pairs QG004 plate 04: 84 unique gene PCR primer pairs Shipping and storage condition Shipped at room temperate Stable for at least 6 months when stored at -20°C Array format GeneCopoeia provides five qPCR array formats (A, B, C, D, and E) suitable for use with the following real- time cyclers. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Histone Deacetylase Inhibitors Promote ATP2A3 Gene Expression in Hepatocellular Carcinoma Cells: P300 As a Transcriptional Regulator T
International Journal of Biochemistry and Cell Biology 113 (2019) 8–16 Contents lists available at ScienceDirect International Journal of Biochemistry and Cell Biology journal homepage: www.elsevier.com/locate/biocel Histone deacetylase inhibitors promote ATP2A3 gene expression in hepatocellular carcinoma cells: p300 as a transcriptional regulator T Andrés Hernández-Oliverasa,b, Eduardo Izquierdo-Torresc, Iván Meneses-Moralesd, ⁎ Gabriela Rodríguezc, Ángel Zarain-Herzbergc, Juan Santiago-Garcíab, a Programa de Doctorado en Ciencias de la Salud, Universidad Veracruzana, Veracruz, Mexico b Instituto de Investigaciones Biológicas, Universidad Veracruzana, Xalapa, Veracruz, Mexico c Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico d Facultad de Ciencias Químicas de la Universidad Juárez del Estado de Durango, Mexico ARTICLE INFO ABSTRACT Keywords: Sarco(endo)plasmic reticulum Ca2+-ATPases (SERCA) expression is reduced or absent in several types of cancer SERCA3 and cancer cell lines; however, their expression and regulation in hepatocellular carcinoma (HCC) are unknown. Chromatin remodeling Histone deacetylase inhibitors (HDACi) increase SERCA3 mRNA expression in gastric and breast cancer cell lines Liver cancer by increasing H3K9ac and binding of Sp1 and Sp3 transcription factors to the promoter; however, the molecular Sodium butyrate mechanism is not fully understood. Our results show that ATP2A3 (SERCA3) gene expression is decreased in Trichostatin A human HCC samples and rat HCC AS-30D cells compared to normal liver, and HCC patients with high expression of ATP2A3 had longer overall survival than those with low expression. Sodium butyrate (NaB) and trichostatin A (TSA) increase SERCA3 mRNA expression in AS-30D cells, whereas SERCA2b mRNA expression did not change. -
Supplementary Table 2
Supplementary Table 2. Differentially Expressed Genes following Sham treatment relative to Untreated Controls Fold Change Accession Name Symbol 3 h 12 h NM_013121 CD28 antigen Cd28 12.82 BG665360 FMS-like tyrosine kinase 1 Flt1 9.63 NM_012701 Adrenergic receptor, beta 1 Adrb1 8.24 0.46 U20796 Nuclear receptor subfamily 1, group D, member 2 Nr1d2 7.22 NM_017116 Calpain 2 Capn2 6.41 BE097282 Guanine nucleotide binding protein, alpha 12 Gna12 6.21 NM_053328 Basic helix-loop-helix domain containing, class B2 Bhlhb2 5.79 NM_053831 Guanylate cyclase 2f Gucy2f 5.71 AW251703 Tumor necrosis factor receptor superfamily, member 12a Tnfrsf12a 5.57 NM_021691 Twist homolog 2 (Drosophila) Twist2 5.42 NM_133550 Fc receptor, IgE, low affinity II, alpha polypeptide Fcer2a 4.93 NM_031120 Signal sequence receptor, gamma Ssr3 4.84 NM_053544 Secreted frizzled-related protein 4 Sfrp4 4.73 NM_053910 Pleckstrin homology, Sec7 and coiled/coil domains 1 Pscd1 4.69 BE113233 Suppressor of cytokine signaling 2 Socs2 4.68 NM_053949 Potassium voltage-gated channel, subfamily H (eag- Kcnh2 4.60 related), member 2 NM_017305 Glutamate cysteine ligase, modifier subunit Gclm 4.59 NM_017309 Protein phospatase 3, regulatory subunit B, alpha Ppp3r1 4.54 isoform,type 1 NM_012765 5-hydroxytryptamine (serotonin) receptor 2C Htr2c 4.46 NM_017218 V-erb-b2 erythroblastic leukemia viral oncogene homolog Erbb3 4.42 3 (avian) AW918369 Zinc finger protein 191 Zfp191 4.38 NM_031034 Guanine nucleotide binding protein, alpha 12 Gna12 4.38 NM_017020 Interleukin 6 receptor Il6r 4.37 AJ002942 -
Region-Specific Transcriptional Changes Following the Three
Molecular Psychiatry (2007) 12, 167–189 & 2007 Nature Publishing Group All rights reserved 1359-4184/07 $30.00 www.nature.com/mp ORIGINAL ARTICLE Region-specific transcriptional changes following the three antidepressant treatments electro convulsive therapy, sleep deprivation and fluoxetine B Conti1, R Maier2, AM Barr1,3, MC Morale1,4,XLu1, PP Sanna1, G Bilbe2, D Hoyer2 and T Bartfai1 1Molecular and Integrative Neuroscience Department, The Harold L Dorris Neurological Research Institute, The Scripps Research Institute, La Jolla, CA, USA and 2Neuroscience Research, Novartis Institutes for Biomedical Research, Basel, Switzerland The significant proportion of depressed patients that are resistant to monoaminergic drug therapy and the slow onset of therapeutic effects of the selective serotonin reuptake inhibitors (SSRIs)/serotonin/noradrenaline reuptake inhibitors (SNRIs) are two major reasons for the sustained search for new antidepressants. In an attempt to identify common underlying mechanisms for fast- and slow-acting antidepressant modalities, we have examined the transcriptional changes in seven different brain regions of the rat brain induced by three clinically effective antidepressant treatments: electro convulsive therapy (ECT), sleep deprivation (SD), and fluoxetine (FLX), the most commonly used slow-onset antidepressant. Each of these antidepressant treatments was applied with the same regimen known to have clinical efficacy: 2 days of ECT (four sessions per day), 24 h of SD, and 14 days of daily treatment of FLX, respectively. Transcriptional changes were evaluated on RNA extracted from seven different brain regions using the Affymetrix rat genome microarray 230 2.0. The gene chip data were validated using in situ hybridization or autoradiography for selected genes. -
Clinical Significance of P‑Class Pumps in Cancer (Review)
ONCOLOGY LETTERS 22: 658, 2021 Clinical significance of P‑class pumps in cancer (Review) SOPHIA C. THEMISTOCLEOUS1*, ANDREAS YIALLOURIS1*, CONSTANTINOS TSIOUTIS1, APOSTOLOS ZARAVINOS2,3, ELIZABETH O. JOHNSON1 and IOANNIS PATRIKIOS1 1Department of Medicine, School of Medicine; 2Department of Life Sciences, School of Sciences, European University Cyprus, 2404 Nicosia, Cyprus; 3College of Medicine, Member of Qatar University Health, Qatar University, 2713 Doha, Qatar Received January 25, 2021; Accepted Apri 12, 2021 DOI: 10.3892/ol.2021.12919 Abstract. P‑class pumps are specific ion transporters involved Contents in maintaining intracellular/extracellular ion homeostasis, gene transcription, and cell proliferation and migration in all 1. Introduction eukaryotic cells. The present review aimed to evaluate the 2. Methodology role of P‑type pumps [Na+/K+ ATPase (NKA), H+/K+ ATPase 3. NKA (HKA) and Ca2+‑ATPase] in cancer cells across three fronts, 4. SERCA pump namely structure, function and genetic expression. It has 5. HKA been shown that administration of specific P‑class pumps 6. Clinical studies of P‑class pump modulators inhibitors can have different effects by: i) Altering pump func‑ 7. Concluding remarks and future perspectives tion; ii) inhibiting cell proliferation; iii) inducing apoptosis; iv) modifying metabolic pathways; and v) induce sensitivity to chemotherapy and lead to antitumor effects. For example, 1. Introduction the NKA β2 subunit can be downregulated by gemcitabine, resulting in increased apoptosis of cancer cells. The sarco‑ The movement of ions across a biological membrane is a endoplasmic reticulum calcium ATPase can be inhibited by crucial physiological process necessary for maintaining thapsigargin resulting in decreased prostate tumor volume, cellular homeostasis. -
©Ferrata Storti Foundation
Original Articles T-cell/histiocyte-rich large B-cell lymphoma shows transcriptional features suggestive of a tolerogenic host immune response Peter Van Loo,1,2,3 Thomas Tousseyn,4 Vera Vanhentenrijk,4 Daan Dierickx,5 Agnieszka Malecka,6 Isabelle Vanden Bempt,4 Gregor Verhoef,5 Jan Delabie,6 Peter Marynen,1,2 Patrick Matthys,7 and Chris De Wolf-Peeters4 1Department of Molecular and Developmental Genetics, VIB, Leuven, Belgium; 2Department of Human Genetics, K.U.Leuven, Leuven, Belgium; 3Bioinformatics Group, Department of Electrical Engineering, K.U.Leuven, Leuven, Belgium; 4Department of Pathology, University Hospitals K.U.Leuven, Leuven, Belgium; 5Department of Hematology, University Hospitals K.U.Leuven, Leuven, Belgium; 6Department of Pathology, The Norwegian Radium Hospital, University of Oslo, Oslo, Norway, and 7Department of Microbiology and Immunology, Rega Institute for Medical Research, K.U.Leuven, Leuven, Belgium Citation: Van Loo P, Tousseyn T, Vanhentenrijk V, Dierickx D, Malecka A, Vanden Bempt I, Verhoef G, Delabie J, Marynen P, Matthys P, and De Wolf-Peeters C. T-cell/histiocyte-rich large B-cell lymphoma shows transcriptional features suggestive of a tolero- genic host immune response. Haematologica. 2010;95:440-448. doi:10.3324/haematol.2009.009647 The Online Supplementary Tables S1-5 are in separate PDF files Supplementary Design and Methods One microgram of total RNA was reverse transcribed using random primers and SuperScript II (Invitrogen, Merelbeke, Validation of microarray results by real-time quantitative Belgium), as recommended by the manufacturer. Relative reverse transcriptase polymerase chain reaction quantification was subsequently performed using the compar- Ten genes measured by microarray gene expression profil- ative CT method (see User Bulletin #2: Relative Quantitation ing were validated by real-time quantitative reverse transcrip- of Gene Expression, Applied Biosystems). -
Supplementary Table 1. Mutated Genes That Contain Protein Domains Identified Through Mutation Enrichment Analysis
Supplementary Table 1. Mutated genes that contain protein domains identified through mutation enrichment analysis A. Breast cancers InterPro ID Mutated genes (number of mutations) IPR000219 ARHGEF4(2), ECT2(1), FARP1(1), FLJ20184(1), MCF2L2(1), NET1(1), OBSCN(5), RASGRF2(2), TRAD(1), VAV3(1) IPR000225 APC2(2), JUP(1), KPNA5(2), SPAG6(1) IPR000357 ARFGEF2(2), CMYA4(1), DRIM(2), JUP(1), KPNA5(2), PIK3R4(1), SPAG6(1) IPR000533 AKAP9(2), C10orf39(1), C20orf23(1), CUTL1(1), HOOK1(1), HOOK3(1), KTN1(2), LRRFIP1(3), MYH1(3), MYH9(2), NEF3(1), NF2(1), RSN(1), TAX1BP1(1), TPM4(1) IPR000694 ADAM12(3), ADAMTS19(1), APC2(2), APXL(1), ARID1B(1), BAT2(2), BAT3(1), BCAR1(1), BCL11A(2), BCORL1(1), C14orf155(3), C1orf2(1), C1QB(1), C6orf31(1), C7orf11(1), CD2(1), CENTD3(3), CHD5(3), CIC(3), CMYA1(2), COL11A1(3), COL19A1(2), COL7A1(3), DAZAP1(1), DBN1(3), DVL3(1), EIF5(1), FAM44A(1), FAM47B(1), FHOD1(1), FLJ20584(1), G3BP2(2), GAB1(2), GGA3(1), GLI1(3), GPNMB(2), GRIN2D(3), HCN3(1), HOXA3(2), HOXA4(1), IRS4(1), KCNA5(1), KCNC2(1), LIP8(1), LOC374955(1), MAGEE1(2), MICAL1(2), MICAL‐L1(1), MLLT2(1), MMP15(1), N4BP2(1), NCOA6(2), NHS(1), NUP214(3), ODZ1(3), PER1(2), PER2(1), PHC1(1), PLXNB1(1), PPM1E(2), RAI17(2), RAPH1(2), RBAF600(2), SCARF2(1), SEMA4G(1), SLC16A2(1), SORBS1(1), SPEN(2), SPG4(1), TBX1(1), TCF1(2), TCF7L1(1), TESK1(1), THG‐1(1), TP53(18), TRIF(1), ZBTB3(2), ZNF318(2) IPR000909 CENTB1(2), PLCB1(1), PLCG1(1) IPR000998 AEGP(3), EGFL6(2), PRSS7(1) IPR001140 ABCB10(2), ABCB6(1), ABCB8(2) IPR001164 ARFGAP3(1), CENTB1(2), CENTD3(3), CENTG1(2) IPR001589 -
Epigenetic Mechanisms Are Involved in the Oncogenic Properties of ZNF518B in Colorectal Cancer
Epigenetic mechanisms are involved in the oncogenic properties of ZNF518B in colorectal cancer Francisco Gimeno-Valiente, Ángela L. Riffo-Campos, Luis Torres, Noelia Tarazona, Valentina Gambardella, Andrés Cervantes, Gerardo López-Rodas, Luis Franco and Josefa Castillo SUPPLEMENTARY METHODS 1. Selection of genomic sequences for ChIP analysis To select the sequences for ChIP analysis in the five putative target genes, namely, PADI3, ZDHHC2, RGS4, EFNA5 and KAT2B, the genomic region corresponding to the gene was downloaded from Ensembl. Then, zoom was applied to see in detail the promoter, enhancers and regulatory sequences. The details for HCT116 cells were then recovered and the target sequences for factor binding examined. Obviously, there are not data for ZNF518B, but special attention was paid to the target sequences of other zinc-finger containing factors. Finally, the regions that may putatively bind ZNF518B were selected and primers defining amplicons spanning such sequences were searched out. Supplementary Figure S3 gives the location of the amplicons used in each gene. 2. Obtaining the raw data and generating the BAM files for in silico analysis of the effects of EHMT2 and EZH2 silencing The data of siEZH2 (SRR6384524), siG9a (SRR6384526) and siNon-target (SRR6384521) in HCT116 cell line, were downloaded from SRA (Bioproject PRJNA422822, https://www.ncbi. nlm.nih.gov/bioproject/), using SRA-tolkit (https://ncbi.github.io/sra-tools/). All data correspond to RNAseq single end. doBasics = TRUE doAll = FALSE $ fastq-dump -I --split-files SRR6384524 Data quality was checked using the software fastqc (https://www.bioinformatics.babraham. ac.uk /projects/fastqc/). The first low quality removing nucleotides were removed using FASTX- Toolkit (http://hannonlab.cshl.edu/fastxtoolkit/).